Evidence map›Paper›PMID 37693050›Full record

ArticleBioengineering & translational medicine2023

A three-dimensional method for morphological analysis and flow velocity estimation in microvasculature on-a-chip.

Alberto Rota, Luca Possenti, Giovanni S Offeddu, Martina Senesi, Adelaide Stucchi, Irene Venturelli, Tiziana Rancati, Paolo Zunino, Roger D Kamm, Maria Laura Costantino

Abstract read
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Article in Bioengineering & translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Alberto RotaLaBS, Chemistry, Materials, and Chemical Engineering "Giulio Natta" Department Politecnico di Milano Milan Italy.ORCID https://orcid.org/0000-0001-9609-6294
Luca PossentiData Science Unit, Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy.ORCID https://orcid.org/0000-0003-4248-6371
Giovanni S OffedduDepartment of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA.
Martina SenesiLaBS, Chemistry, Materials, and Chemical Engineering "Giulio Natta" Department Politecnico di Milano Milan Italy.
Adelaide StucchiLaBS, Chemistry, Materials, and Chemical Engineering "Giulio Natta" Department Politecnico di Milano Milan Italy.
Irene VenturelliLaBS, Chemistry, Materials, and Chemical Engineering "Giulio Natta" Department Politecnico di Milano Milan Italy.
Tiziana RancatiData Science Unit, Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy.
Paolo ZuninoMOX, Department of Mathematics Politecnico di Milano Milan Italy.
Roger D KammDepartment of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA.
Maria Laura CostantinoLaBS, Chemistry, Materials, and Chemical Engineering "Giulio Natta" Department Politecnico di Milano Milan Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Three-dimensional (3D) imaging techniques (e.g., confocal microscopy) are commonly used to visualize in vitro models, especially microvasculature on-a-chip. Conversely, 3D analysis is not the standard method to extract quantitative information from those models. We developed the μVES algorithm to analyze vascularized in vitro models leveraging 3D data. It computes morphological parameters (geometry, diameter, length, tortuosity, eccentricity) and intravascular flow velocity. μVES application to microfluidic vascularized in vitro models shows that they successfully replicate functional features of the microvasculature in vivo in terms of intravascular fluid flow velocity. However, wall shear stress is lower compared to in vivo references. The morphological analysis also highlights the model's physiological similarities (vessel length and tortuosity) and shortcomings (vessel radius and surface-over-volume ratio). The addition of the third dimension in our analysis produced significant differences in the metrics assessed compared to 2D estimations. It enabled the computation of new indices, such as vessel eccentricity. These μVES capabilities can find application in analyses of different in vitro vascular models, as well as in vivo and ex vivo microvasculature.

Indexed as

3D computational analysisdeep learningnetwork morphologysegmentationvasculature‐on‐a‐chip

Identifiers

PMID37693050
PMCPMC10487341

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.